Machine Learning in Python: Building a Linear Regression Model
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In this video, I will be showing you how to build a linear regression model in Python using the scikit-learn package. We will be using the Diabetes dataset (built-in data from scikit-learn) and the Boston Housing (download from GitHub) dataset.
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Hi Professor, I followed the exact same steps as you, but my coef and intercept are different, do you know why? By the way, great presentation.
This is exactly what I was looking for, thank you so much this was such a big help!!!
Fantastic video, thank you.
Nice video, but how do we interpret the results? IOW, what would be the deliverable to our stakeholders? What are the actual predictions?
8:29 is the coefficient the same as the weight?
You are the best professor for explaining , thanks for your content!
Wonderful presentation! Though Im struggling a bit with the loss function and the training/iteration principle. How does this work exactly?
For your first example using the diabetes dataset, I would like to train the data/iterate the data 1000 times, and thereby plot the loss function over a 2-dimensional grid at every 100, 300, 700 and 1000 iteration. How exactly would you do this? Thank you!
hi prof, may I ask ,last stage of scatterplot for boston house model , so x axis is represent the y_test value and y_axis is represent the y_pred value? How do i evaluate from the scatterplot. Could you explain more on plt representation. thank u sir!
I am lazy to comment usally, but this video is very delicious . Keep up with the good work , just subscribed.
Thank you, sir, for making this so easy 🙂
#HappyLearning
Beautiful presentation. Thank you sir.
Where is the line best fit? How to draw a line best fit on the scatter plot graph?
What is the purpose of the train test split function?
7:25 MAE and others
every video you have posted provides value to the audience. Outstanding job. I hope your channel could grow exponentially, as it is deserved.
Hello DataProfessor. I am a beginner in ML and have learned some basic concepts of linear/logistic regression, SVM, ANN, Recommendation systems, Anomaly Detection from ML course by Andrew NG on Coursera. I am looking for some good walkthrough videos like these for picking up libraries like sklearn, tensorflow, etc. Do you have a set of videos that could help me?
P.S – The walkthrough was amazing. thanks for the content.
Glad to have more of your video to watch than usual 😍
So excellent. Thank you so much
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nice!